Non-destructive Evaluation and Inspection (“NDE/I”) technologies generally provide ways to nondestructively scan, image, sense or otherwise evaluate characteristics of materials and/or components. In particular, NDE/I technologies may be used to detect minute flaws and defects in those materials and/or component parts. As such, NDE/I technologies have become increasingly used to help assure structural and functional integrity, safety, and cost effective sustainment of various assets, during both initial manufacture and operational service.
Non-destructive evaluation (“NDE”) data is often based on raw data gathered from NDE data collection devices and may include x-ray images of at least a portion of a part or asset, such as the wing of an aircraft or some other type of part that may be manufactured. NDE data is often large in size, associated with merely a portion of the part, and also must be matched with a particular location on the part. Such large data sets of NDE data become increasingly difficult to manage, particularly if such NDE datasets are collected for many parts manufactured in a manufacturing process. In addition, other types of quality related data, including for example visual inspection data from an inspector, may further complicate management and analysis of NDE data and/or quality related data on a large scale, such as in a manufacturing environment.
To determine wear and tear, structural damage and/or other irregularities of a part may require the analysis of tens (if not hundreds) of individual datasets of NDE data and/or quality related data. This may result in numerous datasets of NDE data and/or quality related data for each manufactured part of a manufacturing process, and thus even more datasets of NDE data and/or quality related data for a plurality of parts manufactured by the manufacturing process. As each dataset is analyzed, this results in large amounts of data that are difficult to categorize and otherwise analyze in whole. Moreover, the NDE data and/or other such quality related data may be discarded after it has been analyzed, and thus there is often little inspection data related to the manufacture of parts over time.
To account for such data management issues, in some conventional systems, NDE data and/or quality related data may be discarded or ignored if such data does not correspond to a part on which a manufacturing defect has been detected. Moreover, in conventional systems, analysis of NDE data and/or quality related data is time consuming due to the cumbersome nature of the data. Hence, when utilizing NDE data and/or other such types of inspection data for parts manufactured in a manufacturing process, the usefulness of such NDE data and/or other such types of inspection data is limited due to the inefficiencies associated with management and analysis of such data.
Consequently, there is a continuing need to manage and analyze inspection data for a manufacturing process.